Magnetic Flux Leakage(MFL) inspection is one of the most efficient non-destructive testing method used to detect anormalies of ferromagnetic materials which is widely used in many industry scenarios. Sizing of the defect from inspection signal is the key problem which is also a classic inverse problem. Reconstruction of corrosion liked defect with high degree of freedom(DOF) is a challenging problem in this area. A multi-agent based reconstruction algorithm(MAR) is proposed in this paper. Instead of designing an algorithm to build the entire defect, the defect region is divided into several segments. Each segment is reconstructed by its corresponding agent. The dimension of reward function is reduced number-of-agent times from the DOF of defect which makes the critic network efficient. Base on MAR, an uni-agent base reconstruction algorithm(UAR) is also given as another solution which requires less computing resources. How to divide the defect and corresponding signal to achieve better performance is also studied. Algorithms proposed in this paper solves the problem of inverse problem of MFL inspection with high DOF. The effectiveness of the algorithms proposed in this paper are validated with simulated data and practical inspection data. The results show that the algorithms proposed in this paper has good reconstruction accuracy with robustness when dealing with defects with high DOF.Note to Practitioners—MFL inspection is the widely used pipeline in-line inspection method. Currently, defects detected are usually quantified as simple cube defects. Since most of the practical defects on industrial pipelines are caused by corrosion which have complex shape, how to quantify defects with more details is not only a research topic but also practical demand. In this paper, MAR and UAR are proposed to reconstruct the depth with 99 degree of freedoms. In addition, the algorithms proposed can be extended to reconstruct the depth with much more higher degree of freedoms by just adopting more agents. The impact of wings data on reconstruction results is also discussed in this paper. The algorithms proposed in this paper focus on 2-D dimension which considers only the length and depth reconstruction problem. In further research, we will try to give solutions to 3-D dimension problem which considers length, width and depth in the same time.

Abstract Location‐based services have attracted significant attention since the concept of Industry 4.0 was proposed and the ‘Internet +’ era began owing to its social and commercial value. Many scholars have attempted to introduce machine learning in indoor fingerprint positioning to improve indoor positioning accuracy, enhance system robustness, reduce costs, and improve the performance of indoor positioning methods. A comprehensive overview of indoor positioning technology, its methods, and classifications is provided. Furthermore, a detailed review of the application of WiFi fingerprinting and machine learning methods in indoor positioning is presented, and the advantages and disadvantages of these methods when applied to indoor positioning are analyzed. This study summarizes the difficulties and challenges encountered by indoor positioning and suggests development directions.

Food safety management is an important issue in human health and social stability. It is prohibitively expensive to monitor all network nodes because of the large scale of food supply networks and the limited monitoring resources. We propose a directed graph-based social network model for design optimization of hazard monitoring network. Following the principles of nodes in social networks, the concepts and calculating methods of k-step observability/controllability and accessible node sets are proposed to measure node accessibility. Based on the above concepts, a 0-1 linear programming model is developed for the selection of the optimal monitoring points. The selected combination of monitoring points can access the whole food supply network with constrained monitoring resources. The model can be solved by using a common commercial software package. We apply the approach to a real local supply network of infant foods to test the validity of the model. A reasonable and satisfactory result is achieved. The numerical analysis of several randomly generated examples with different parameters shows the validity and effectiveness of the recommended approach.

In a wireless sensor network (WSN), each sensor monitors environmental parameters, and reports its readings to a base station, possibly through other nodes. A sensor works in cycles, in each of which it stays active for a fixed duration, and then sleeps until the next cycle. The frequency of such cycles determines the portion of time that a sensor is active, and is the dominant factor on its battery life. The majority of existing work assumes globally synchronized WSN where all sensors have the same frequency. This leads to waste of battery power for applications that entail different accuracy of measurements, or environments where sensor readings have large variability. To overcome this problem, we propose LS, a query processing framework for locally synchronized WSN. We consider that each sensor nihas a distinct sampling frequency fi, which is determined by the application or environment requirements. The complication of LS is that nihas to wake up with a network frequency Fi≥fi, in order to forward messages of other sensors. Our goal is to minimize the sum of Fiwithout delaying packet transmissions. Specifically, given a routing tree, we first present a dynamic programming algorithm that computes the optimal network frequency of each sensor; then, we develop a heuristic for finding the best tree topology, if this is not fixed in advance.

Multistep processing is commonly used for nearest neighbor (NN) and similarity search in applications involving high-dimensional data and/or costly distance computations. Today, many such applications require a proof of result correctness. In this setting, clients issue NN queries to a server that maintains a database signed by a trusted authority. The server returns the NN set along with supplementary information that permits result verification using the data set signature. An adaptation of the multistep NN algorithm incurs prohibitive network overhead due to the transmission of false hits, i.e., records that are not in the NN set, but are nevertheless necessary for its verification. In order to alleviate this problem, we present a novel technique that reduces the size of each false hit. Moreover, we generalize our solution for a distributed setting, where the database is horizontally partitioned over several servers. Finally, we demonstrate the effectiveness of the proposed solutions with real data sets of various dimensionalities.